OpenRaiser/Intern-Atlas huggingface dataset:
SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance
Venue: arXiv cs.CV 2019
Year: 2019

The log-ratio (LR) operator has been widely employed to generate the difference image for synthetic aperture radar (SAR) image change detection. However, the difference image generated by this pixel-wise operator can be subject to SAR images speckle and unavoidable registration errors between bitemporal SAR images. In this letter, we proposed a spatial metric learning method to obtain a difference image more robust to the speckle by learning a metric from a set of constraint pairs. In the proposed method, spatial context is considered in constructing constraint pairs, each of which consists of patches in the same location of bitemporal SAR images. Then, a semi-definite positive metric matrix $\bf M$ can be obtained by the optimization with the max-margin criterion. Finally, we verify our proposed method on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-art methods.
A simple and effective postprocessing method for image classification
Venue: arXiv cs.CV 2019
Year: 2019

Whether it is computer vision, natural language processing or speech recognition, the essence of these applications is to obtain powerful feature representations that make downstream applications completion more efficient. Taking image recognition as an example, whether it is hand-crafted low-level feature representation or feature representation extracted by a convolutional neural networks(CNNs), the goal is to extract features that better represent image features, thereby improving classification accuracy. However, we observed that image feature representations share a large common vector and a few top dominating directions. To address this problems, we propose a simple but effective postprocessing method to render off-the-shelf feature representations even stronger by eliminating the common mean vector from off-the-shelf feature representations. The postprocessing is empirically validated on a variety of datasets and feature extraction methods.such as VGG, LBP, and HOG. Some experiments show that the features that have been post-processed by postprocessing algorithm can get better results than original ones.
Development of a robotic system for automatic organic chemistry synthesis
Venue: arXiv cs.RO 2019
Year: 2019

Automated chemical synthesis carries great promises of safety, efficiency and reproducibility for both research and industry laboratories. Current approaches are based on specifically-designed automation systems, which present two major drawbacks: (i) existing apparatus must be modified to be integrated into the automation systems; (ii) such systems are not flexible and would require substantial re-design to handle new reactions or procedures. In this paper, we propose a system based on a robot arm which, by mimicking the motions of human chemists, is able to perform complex chemical reactions without any modifications to the existing setup used by humans. The system is capable of precise liquid handling, mixing, filtering, and is flexible: new skills and procedures could be added with minimum effort. We show that the robot is able to perform a Michael reaction, reaching a yield of 34%, which is comparable to that obtained by a junior chemist (undergraduate student in Chemistry).
Camouflage Design of Analysis Based on HSV Color Statistics and K-means Clustering
Venue: arXiv cs.GR 2019
Year: 2019

Since ancient times, it has been essential to adopting camouflage on the battlefield, whether it is in the forefront, in-depth or the rear. The traditional evaluation method is made up of people opinion. By watching target or looking at the pictures, and determine the effect of camouflage, so it can be more influenced by man's subjective factors. And now, in order to objectively reflect the camouflage effect, we set up a model through using images similarity to evaluate camouflage effect. Image similarity comparison is divided into two main image feature comparison: image color features and texture features of images. We now using computer design camouflage, camouflage pattern design is divided into two aspects of design color and design plaques. For the design of the color, we based on HSV color model, and as for the design of plague, the key steps are the background color edge extraction, we adopt algorithm based on k-means clustering analysis of the method of background color edge extraction.
Ethical Interviews in Software Engineering
Venue: arXiv cs.SE 2019
Year: 2019

Background: Despite a long history, numerous laws and regulations, ethics remains an unnatural topic for many software engineering researchers. Poor research ethics may lead to mistrust of research results, lost funding and retraction of publications. A core principle for research ethics is confidentiality, and anonymization is a standard approach to guarantee it. Many guidelines for qualitative software engineering research, and for qualitative research in general, exist, but these do not penetrate how and why to anonymize interview data. Aims: In this paper we aim to identify ethical guidelines for software engineering interview studies involving industrial practitioners. Method: By learning from previous experiences and listening to the authority of existing guidelines in the more mature field of medicine as well as in software engineering, a comprehensive set of checklists for interview studies was distilled. Results: The elements of an interview study were identified and ethical considerations and recommendations for each step were produced, in particular with respect to anonymization. Important ethical principles are: consent, beneficence, confidentiality, scientific value, researcher skill, justice, respect for law, and ethical reviews. Conclusions: The most important contribution of this study is the set of checklists for ethical interview studies. Future work is needed to refine these guidelines with respect to legal aspects and ethical boards.
Novelty Messages Filtering for Multi Agent Privacy-preserving Planning
Venue: arXiv cs.AI 2019
Year: 2019

In multi-agent planning, agents jointly compute a plan that achieves mutual goals, keeping certain information private to the individual agents. Agents' coordination is achieved through the transmission of messages. These messages can be a source of privacy leakage as they can permit a malicious agent to collect information about other agents' actions and search states. In this paper, we investigate the usage of novelty techniques in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that the use of novelty based techniques can significantly reduce the number of messages transmitted among agents, better preserving their privacy and improving their performance. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art. Finally, we evaluate the robustness of our approach, considering different delays in the transmission of messages as they would occur in overloaded networks, due for example to massive attacks or critical situations.
Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
Venue: arXiv cs.CV 2019
Year: 2019

Medical imaging is playing a more and more important role in clinics. However, there are several issues in different imaging modalities such as slow imaging speed in MRI, radiation injury in CT and PET. Therefore, accelerating MRI, reducing radiation dose in CT and PET have been ongoing research topics since their invention. Usually, acquiring less data is a direct but important strategy to address these issues. However, less acquisition usually results in aliasing artifacts in reconstructions. Recently, deep learning (DL) has been introduced in medical image reconstruction and shown potential on significantly speeding up MR reconstruction and reducing radiation dose. In this paper, we propose a general framework on combining the reconstruction model with deep learning to maximize the potential of deep learning and model-based reconstruction, and give the examples to demonstrate the performance and requirements of unrolling different algorithms using deep learning.
MaGPoS -- A novel decentralized consensus mechanism combining magnetism and proof of stake
Venue: arXiv cs.DC 2019
Year: 2019

We describe MaGPoS, a novel consensus mechanism which is well suited to decentralized blockchain based protocols. MaGPoS is based on a combination of the well known physics of nano-scale magnetism, and previous implementations of proof of stake. This system has been studied by hundreds of thousands of scientists worldwide for over a hundred years, giving it an extreme level of reliability that is needed for a consensus mechanism. We start by explaining the physics, and study the properties that make it particularly beneficial for use in a consensus mechanism. We then show how to apply the physical model to a decentralized network of nodes, each with their own copy of a blockchain. After this, we describe some example calculations that a node in the decentralized network would make, and provide pseudo code for implementation. Finally, we discuss the how the model achieves all of the important properties that one expects of a consensus mechanism.
Deep Fuzzy Systems
Venue: arXiv cs.OH 2019
Year: 2019

An investigation of deep fuzzy systems is presented in this paper. A deep fuzzy system is represented by recursive fuzzy systems from an input terminal to output terminal. Recursive fuzzy systems are sequences of fuzzy grade memberships obtained using fuzzy transmition functions and recursive calls to fuzzy systems. A recursive fuzzy system which calls a fuzzy system n times includes fuzzy chains to evaluate the final grade membership of this recursive system. A connection matrix which includes recursive calls are used to represent recursive fuzzy systems.
Reduced I/O Latency with Futures
Venue: arXiv cs.DC 2019
Year: 2019

Task parallelism research has traditionally focused on optimizing computation-intensive applications. Due to the proliferation of commodity parallel processors, there has been recent interest in supporting interactive applications. Such interactive applications frequently rely on I/O operations that may incur significant latency. In order to increase performance, when a particular thread of control is blocked on an I/O operation, ideally we would like to hide this latency by using the processing resources to do other ready work instead of blocking or spin waiting on this I/O. There has been limited prior work on hiding this latency and only one result that provides a theoretical bound for interactive applications that use I/Os. In this work, we propose a method for hiding the latency of I/O operations by using the futures abstraction. We provide a theoretical analysis of our algorithm that shows our algorithm provides better execution time guarantees than prior work. We also implemented the algorithm in a practically efficient prototype library that runs on top of the Cilk-F runtime, a runtime system that supports futures within the context of the Cilk Plus language, and performed experiments that demonstrate the efficiency of our implementation.
Driving Path Stability in VANETs
Venue: arXiv cs.NI 2019
Year: 2019

Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles, that influence the stability of communication. To overcome this issue, it is necessary to increase the routing protocols performances, by keeping only the stable path during the communication. The effective solutions that have been investigated in the literature are based on the link prediction to avoid broken links. In this paper, we propose a new solution based on machine learning concept for link prediction, using LR and Support Vector Regression (SVR) which is a variant of the Support Vector Machine (SVM) algorithm. SVR allows predicting the movements of the vehicles in the network which gives us a decision for the link state at a future time. We study the performance of SVR by comparing the generated prediction values against real movement traces of different vehicles in various mobility scenarios, and to show the effectiveness of the proposed method, we calculate the error rate. Finally, we compare this new SVR method with Lagrange interpolation solution.
How the Avengers assemble: Ecological modelling of effective cast sizes for movies
Venue: arXiv cs.SI 2019
Year: 2019

The number of characters in a movie is an interesting feature. However, it is non-trivial to measure directly. Naive metrics such as the number of credited characters vary wildly. Here, we show that a metric based on the notion of "ecological diversity" as expressed through a Shannon-entropy based metric can characterise the number of characters in a movie, and is useful in taxonomic classification. We also show how the metric can be generalised using Jensen-Shannon divergence to provide a measure of the similarity of characters appearing in different movies, for instance of use in recommender systems, e.g., Netflix. We apply our measures to the Marvel Cinematic Universe (MCU), and show what they teach us about this highly successful franchise of movies. In particular, these measures provide a useful predictor of "success" for films in the MCU, as well as a natural means to understand the relationships between the stories in the overall film arc.
Data Interpolating Prediction: Alternative Interpretation of Mixup
Venue: arXiv cs.LG 2019
Year: 2019

Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks. However, this strategy is not always effective due to the gap between augmented samples for training and original samples for testing. This gap may prevent a classifier from learning the optimal decision boundary and increase the generalization error. To overcome this problem, we propose an alternative framework called Data Interpolating Prediction (DIP). Unlike common data augmentations, we encapsulate the sample-mixing process in the hypothesis class of a classifier so that train and test samples are treated equally. We derive the generalization bound and show that DIP helps to reduce the original Rademacher complexity. Also, we empirically demonstrate that DIP can outperform existing Mixup.
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects
Venue: arXiv cs.LG 2019
Year: 2019

Visual question answering (VQA) models have been shown to over-rely on linguistic biases in VQA datasets, answering questions "blindly" without considering visual context. Adversarial regularization (AdvReg) aims to address this issue via an adversary sub-network that encourages the main model to learn a bias-free representation of the question. In this work, we investigate the strengths and shortcomings of AdvReg with the goal of better understanding how it affects inference in VQA models. Despite achieving a new state-of-the-art on VQA-CP, we find that AdvReg yields several undesirable side-effects, including unstable gradients and sharply reduced performance on in-domain examples. We demonstrate that gradual introduction of regularization during training helps to alleviate, but not completely solve, these issues. Through error analyses, we observe that AdvReg improves generalization to binary questions, but impairs performance on questions with heterogeneous answer distributions. Qualitatively, we also find that regularized models tend to over-rely on visual features, while ignoring important linguistic cues in the question. Our results suggest that AdvReg requires further refinement before it can be considered a viable bias mitigation technique for VQA.
A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning
Venue: arXiv cs.RO 2019
Year: 2019

Tactical decision making is a critical feature for advanced driving systems, that incorporates several challenges such as complexity of the uncertain environment and reliability of the autonomous system. In this work, we develop a multi-modal architecture that includes the environmental modeling of ego surrounding and train a deep reinforcement learning (DRL) agent that yields consistent performance in stochastic highway driving scenarios. To this end, we feed the occupancy grid of the ego surrounding into the DRL agent and obtain the high-level sequential commands (i.e. lane change) to send them to lower-level controllers. We will show that dividing the autonomous driving problem into a multi-layer control architecture enables us to leverage the AI power to solve each layer separately and achieve an admissible reliability score. Comparing with end-to-end approaches, this architecture enables us to end up with a more reliable system which can be implemented in actual self-driving cars.
Regional based query in graph active learning
Venue: arXiv cs.LG 2019
Year: 2019

Graph convolution networks (GCN) have emerged as the leading method to classify node classes in networks, and have reached the highest accuracy in multiple node classification tasks. In the absence of available tagged samples, active learning methods have been developed to obtain the highest accuracy using the minimal number of queries to an oracle. The current best active learning methods use the sample class uncertainty as selection criteria. However, in graph based classification, the class of each node is often related to the class of its neighbors. As such, the uncertainty in the class of a node's neighbor may be a more appropriate selection criterion. We here propose two such criteria, one extending the classical uncertainty measure, and the other extending the page-rank algorithm. We show that the latter is optimal when the fraction of tagged nodes is low, and when this fraction grows to one over the average degree, the regional uncertainty performs better than all existing methods. While we have tested this methods on graphs, such methods can be extended to any classification problem, where a distance metrics can be defined between the input samples. All the code used can be accessed at : https://github.com/louzounlab/graph-al All the datasets used can be accessed at : https://github.com/louzounlab/DataSets
Designing Game of Theorems
Venue: arXiv cs.AI 2019
Year: 2019

"Theorem proving is similar to the game of Go. So, we can probably improve our provers using deep learning, like DeepMind built the super-human computer Go program, AlphaGo." Such optimism has been observed among participants of AITP2017. But is theorem proving really similar to Go? In this paper, we first identify the similarities and differences between them and then propose a system in which various provers keep competing against each other and changing themselves until they prove conjectures provided by users.
Tactile Internet and its Contribution in the Development of Smart Cities
Venue: arXiv cs.NI 2019
Year: 2019

The Tactile Internet (TI) is an emerging technology next to the Internet of Things (IoT). It is a revolution to develop the smart cities, communities and cultures in the future. This technology will allow the real-time interaction between human and machines as well as machine-to-machine with the 1ms challenge to achieve in round trip latency. The term TI is defined by International Telecommunication Union (ITU) in August 2014. The TI provides fast, reliable, secure and available internet network that is the requirements of the smart cities in 5G. Tactile internet can develop the part of world where the machines are strong and human are weak. It increases the power of machines so that the value of human power will increase automatically. In this framework, we have presented the idea of tactile internet for the next generation smart cities. This research will provide a high-performance reliable framework for the internet of smart devices to communicate with each other in a real-time (1ms round trip) using IEEE 1918.1 standard. The objective of this research is expected to bring a new dimension in the research of the smart cities.
Measuring the Importance of User-Generated Content to Search Engines
Venue: arXiv cs.CY 2019
Year: 2019

Search engines are some of the most popular and profitable intelligent technologies in existence. Recent research, however, has suggested that search engines may be surprisingly dependent on user-created content like Wikipedia articles to address user information needs. In this paper, we perform a rigorous audit of the extent to which Google leverages Wikipedia and other user-generated content to respond to queries. Analyzing results for six types of important queries (e.g. most popular, trending, expensive advertising), we observe that Wikipedia appears in over 80% of results pages for some query types and is by far the most prevalent individual content source across all query types. More generally, our results provide empirical information to inform a nascent but rapidly-growing debate surrounding a highly-consequential question: Do users provide enough value to intelligent technologies that they should receive more of the economic benefits from intelligent technologies?
CBC Approach for Evaluating Potential SaaS on the Cloud
Venue: arXiv cs.SE 2019
Year: 2019

The cloud computing is evolving as a key computing platform for sharing resources like infrastructure, platform, software etc. This has proven to be an essential requirement for extending many existing applications. Software as a service (SaaS) is referred as on-demand software supplied by service providers in which software and associated data are hosted on the cloud and it can be accessed by service users using a thin client via a web browser. SaaS is commonly utilized and it provides many benefits to service users. To realize these benefits, it is essential to evaluate potential quality of SaaS, not only to the service users but also to the service providers. They have to evaluate their services against requirements of service users. The existing evaluation models are focusing only on quality attributes of SaaS. In this paper, a new evaluation model is proposed based on the data mining technique of Constraint Based Clustering (CBC). The proposed model gives emphasis on potential requirements of service users along with quality attributes of services.
Program Generation for Linear Algebra Using Multiple Layers of DSLs
Venue: arXiv cs.MS 2019
Year: 2019

Numerical software in computational science and engineering often relies on highly-optimized building blocks from libraries such as BLAS and LAPACK, and while such libraries provide portable performance for a wide range of computing architectures, they still present limitations in terms of flexibility. We advocate a domain-specific program generator capable of producing library routines tailored to the specific needs of the application in terms of sizes, interface, and target architecture.
Zero-shot Learning and Knowledge Transfer in Music Classification and Tagging
Venue: arXiv cs.MM 2019
Year: 2019

Music classification and tagging is conducted through categorical supervised learning with a fixed set of labels. In principle, this cannot make predictions on unseen labels. Zero-shot learning is an approach to solve the problem by using side information about the semantic labels. We recently investigated this concept of zero-shot learning in music classification and tagging task by projecting both audio and label space on a single semantic space. In this work, we extend the work to verify the generalization ability of zero-shot learning model by conducting knowledge transfer to different music corpora.